The present disclosure presents systems and methods of tagging TRISO-fueled pebbles. One such method comprises acquiring an ionizing radiation image of a TRISO-fueled pebble; analyzing, using a machine learning algorithm, the acquired image of the TRISO-fueled pebble to identify a unique pattern of particle distributions that is visible in the acquired image of the TRISO-fueled pebble; deriving a TRISO-particle distribution fingerprint for the TRISO-fueled pebble that corresponds to the unique pattern of particle distributions; assigning an individual identifier to the TRISO-fueled pebble that corresponds to a TRISO-particle distribution fingerprint; and storing the TRISO-particle distribution fingerprint and the individual identifier for the TRISO-fueled pebble in an image database, wherein the image database stores a plurality of TRISO-particle distribution fingerprints and individual identifiers for a plurality of TRISO-fueled pebbles. Other systems and methods are also presented.

Provided is a statistical overpower penalty calculation system for a generic thermal margin analysis model, the system including: a random number generating unit generating a plurality of random numbers; an power distribution generating unit generating power information of an axial direction and a radial direction for a core burnup; an operating condition generating unit extracting an arbitrary value for a plurality of operating conditions from the random number generated above; a POL calculating unit calculating a POL of a reload core thermal margin analysis model and a POL of a generic thermal margin analysis model and calculating a plurality of the overpower penalties through the POLs; and a statistics processing unit calculating tolerance limit values according to the core burnup by statistically analyzing a distribution formed of the plurality of the overpower penalties and selecting a smallest tolerance limit value as a representative value of the overpower penalties.

Provided is a statistical overpower penalty calculation system for a generic thermal margin analysis model, the system including: a random number generating unit generating a plurality of random numbers; an power distribution generating unit generating power information of an axial direction and a radial direction for a core burnup; an operating condition generating unit extracting an arbitrary value for a plurality of operating conditions from the random number generated above; a POL calculating unit calculating a POL of a reload core thermal margin analysis model and a POL of a generic thermal margin analysis model and calculating a plurality of the overpower penalties through the POLs; and a statistics processing unit calculating tolerance limit values according to the core burnup by statistically analyzing a distribution formed of the plurality of the overpower penalties and selecting a smallest tolerance limit value as a representative value of the overpower penalties.

Most commercial power reactors in the world, so called second generation of nuclear power plants (NPP), were designed in 1960s and 1970s. Due to technology constrains, these NPP's nuclear fuel burnup data are calculated as a whole of a fuel assembly (FA) based on the total core power output during certain period of time and the theoretical physics calculation of the thermal neutron flux (TNF) distribution in the reactor core. This traditional burnup calculation based on theoretical TNF 3-D distribution for each FA in the core is far less accurate in term of pin-point burnup data along the entire length of a FA. Therefore, the most contribution factor to fuel failure event, e.g. the accurate burnup data at a fine grained location along a FA, could not be obtained by these existing methods and practice in these NPPs.

This invention applies the modern machine learning and artificial intelligent methods to provide a much finer-grained TNF 3D distribution prediction for these second generation NPPs. With this pin-point TNF data along each FA's length, the maximum burnup locations in the entire core can be determined. This will result a more accurate method for determine the fuel failure locations after fuel failure events.

A nuclear fuel assay system comprises a nuclear fuel assembly comprising structures containing nuclear fuel, and a neutron collar surrounding sides of the nuclear fuel assembly and comprising pressurized .sup.4He scintillation detectors. A system for assaying nuclear fuel, and a method of quantifying nuclear material are also described.